Timeframe
5m
Direction
Long Only
Stoploss
-3.0%
Trailing Stop
No
ROI
0m: 6.0%
Interface Version
3
Startup Candles
N/A
Indicators
2
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these imports ---
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Dict, Optional, Union, Tuple
from freqtrade.strategy import (
IStrategy,
Trade,
Order,
PairLocks,
informative, # @informative decorator
# Hyperopt Parameters
BooleanParameter,
CategoricalParameter,
DecimalParameter,
IntParameter,
RealParameter,
# timeframe helpers
timeframe_to_minutes,
timeframe_to_next_date,
timeframe_to_prev_date,
# Strategy helper functions
merge_informative_pair,
stoploss_from_absolute,
stoploss_from_open,
AnnotationType,
)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
from technical import qtpylib
import logging
logger = logging.getLogger(__name__)
class RandomEntry(IStrategy):
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 3
# Optimal timeframe for the strategy.
timeframe = "5m"
# Can this strategy go short?
can_short: bool = True
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.06,
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.03
# Trailing stoploss
trailing_stop = False
# trailing_only_offset_is_reached = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# These values can be overridden in the config.
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 1
# Strategy parameters
# Define parameters for random entry - probability of generating a long signal (0.5 = 50%)
long_probability = DecimalParameter(0.1, 0.9, default=0.6, space="buy", optimize=True)
short_probability = DecimalParameter(0.1, 0.9, default=0.4, space="buy", optimize=True)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Generate a random number between 0 and 1 for each candle
dataframe['random_value'] = np.random.random(size=len(dataframe))
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema200'] = ta.EMA(dataframe, timeperiod=100)
dataframe['rsi'] = ta.RSI(dataframe)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# dataframe['enter_long'] = 0
# dataframe['enter_short'] = 0
# if((dataframe['ema50'] > dataframe['ema200']).all()):
# dataframe.loc[(dataframe['random_value'] < self.long_probability.value), 'enter_long'] = 1
# dataframe.loc[(dataframe['random_value'] < self.short_probability.value), 'enter_short'] = 1
# else:
# dataframe.loc[(dataframe['random_value'] < self.short_probability.value), 'enter_long'] = 1
# dataframe.loc[(dataframe['random_value'] < self.long_probability.value), 'enter_short'] = 1
dataframe.loc[(dataframe["ema50"] < dataframe["ema200"]) & (dataframe["rsi"] > 30), "enter_short"] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe